Automatic Reconstruction of Parametric, Volumetric Building Models from 3D Point Clouds

Planning, construction, modification, and analysis of buildings requires means of representing a building's physical structure and related semantics in a meaningful way. With the rise of novel technologies and increasing requirements in the architecture, engineering and construction (AEC) domain, two general concepts for representing buildings have gained particular attention in recent years. First, the concept of Building Information Modeling (BIM) is increasingly used as a modern means for representing and managing a building's as-planned state digitally, including not only a geometric model but also various additional semantic properties. Second, point cloud measurements are now widely used for capturing a building's as-built condition by means of laser scanning techniques. A particular challenge and topic of current research are methods for combining the strengths of both point cloud measurements and Building Information Modeling concepts to quickly obtain accurate building models from measured data. In this thesis, we present our recent approaches to tackle the intermeshed challenges of automated indoor point cloud interpretation using targeted segmentation methods, and the automatic reconstruction of high-level, parametric and volumetric building models as the basis for further usage in BIM scenarios. In contrast to most reconstruction methods available at the time, we fundamentally base our approaches on BIM principles and standards, and overcome critical limitations of previous approaches in order to reconstruct globally plausible, volumetric, and parametric models.Automatische Rekonstruktion von parametrischen, volumetrischen Gebäudemodellen aus 3D Punktwolken Für die Planung, Konstruktion, Modifikation und Analyse von Gebäuden werden Möglichkeiten zur sinnvollen Repräsentation der physischen Gebäudestruktur sowie dazugehöriger Semantik benötigt. Mit dem Aufkommen neuer Technologien und steigenden Anforderungen im Bereich von Architecture, Engineering and Construction (AEC) haben zwei Konzepte für die Repräsentation von Gebäuden in den letzten Jahren besondere Aufmerksamkeit erlangt. Erstens wird das Konzept des Building Information Modeling (BIM) zunehmend als ein modernes Mittel zur digitalen Abbildung und Verwaltung "As-Planned"-Zustands von Gebäuden verwendet, welches nicht nur ein geometrisches Modell sondern auch verschiedene zusätzliche semantische Eigenschaften beinhaltet. Zweitens werden Punktwolkenmessungen inzwischen häufig zur Aufnahme des "As-Built"-Zustands mittels Laser-Scan-Techniken eingesetzt. Eine besondere Herausforderung und Thema aktueller Forschung ist die Entwicklung von Methoden zur Vereinigung der Stärken von Punktwolken und Konzepten des Building Information Modeling um schnell akkurate Gebäudemodelle aus den gemessenen Daten zu erzeugen. In dieser Dissertation präsentieren wir unsere aktuellen Ansätze um die miteinander verwobenen Herausforderungen anzugehen, Punktwolken mithilfe geeigneter Segmentierungsmethoden automatisiert zu interpretieren, sowie hochwertige, parametrische und volumetrische Gebäudemodelle als Basis für die Verwendung im BIM-Umfeld zu rekonstruieren. Im Gegensatz zu den meisten derzeit verfügbaren Rekonstruktionsverfahren basieren unsere Ansätze grundlegend auf Prinzipien und Standards aus dem BIM-Umfeld und überwinden kritische Einschränkungen bisheriger Ansätze um vollständig plausible, volumetrische und parametrische Modelle zu erzeugen.</p

A FLEXIBLE METHODOLOGY FOR OUTDOOR/INDOOR BUILDING RECONSTRUCTION FROM OCCLUDED POINT CLOUDS

Terrestrial Laser Scanning data are increasingly used in building survey not only in cultural heritage domain but also for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning generally not directly ready for the generation of such models. A time-consuming manual modelling phase has to be taken into account. In addition the large presence of occlusion and clutter may turn out in low-quality building models when state-of-the-art automatic modelling procedures are applied. This paper presents an automated procedure to convert raw point clouds into semantically-enriched building models. The developed method mainly focuses on a geometrical complexity typical of modern buildings with clear prevalence of planar features A characteristic of this methodology is the possibility to work with outdoor and indoor building environments. In order to operate under severe occlusions and clutter a couple of completion algorithms were designed to generate a plausible and reliable model. Finally, some examples of the developed modelling procedure are presented and discussed

The robot's vista space : a computational 3D scene analysis

Swadzba A. The robot's vista space : a computational 3D scene analysis. Bielefeld (Germany): Bielefeld University; 2011.The space that can be explored quickly from a fixed view point without locomotion is known as the vista space. In indoor environments single rooms and room parts follow this definition. The vista space plays an important role in situations with agent-agent interaction as it is the directly surrounding environment in which the interaction takes place. A collaborative interaction of the partners in and with the environment requires that both partners know where they are, what spatial structures they are talking about, and what scene elements they are going to manipulate. This thesis focuses on the analysis of a robot's vista space. Mechanisms for extracting relevant spatial information are developed which enable the robot to recognize in which place it is, to detect the scene elements the human partner is talking about, and to segment scene structures the human is changing. These abilities are addressed by the proposed holistic, aligned, and articulated modeling approach. For a smooth human-robot interaction, the computed models should be aligned to the partner's representations. Therefore, the design of the computational models is based on the combination of psychological results from studies on human scene perception with basic physical properties of the perceived scene and the perception itself. The holistic modeling realizes a categorization of room percepts based on the observed 3D spatial layout. Room layouts have room type specific features and fMRI studies have shown that some of the human brain areas being active in scene recognition are sensitive to the 3D geometry of a room. With the aligned modeling, the robot is able to extract the hierarchical scene representation underlying a scene description given by a human tutor. Furthermore, it is able to ground the inferred scene elements in its own visual perception of the scene. This modeling follows the assumption that cognition and language schematize the world in the same way. This is visible in the fact that a scene depiction mainly consists of relations between an object and its supporting structure or between objects located on the same supporting structure. Last, the articulated modeling equips the robot with a methodology for articulated scene part extraction and fast background learning under short and disturbed observation conditions typical for human-robot interaction scenarios. Articulated scene parts are detected model-less by observing scene changes caused by their manipulation. Change detection and background learning are closely coupled because change is defined phenomenologically as variation of structure. This means that change detection involves a comparison of currently visible structures with a representation in memory. In range sensing this comparison can be nicely implement as subtraction of these two representations. The three modeling approaches enable the robot to enrich its visual perceptions of the surrounding environment, the vista space, with semantic information about meaningful spatial structures useful for further interaction with the environment and the human partner

A Review of Panoptic Segmentation for Mobile Mapping Point Clouds

3D point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3D scene understanding, building on the rapid advances of semantic segmentation due to the advent of deep 3D neural networks. Yet, to date there is very little work about panoptic segmentation of outdoor mobile-mapping data, and no systematic comparisons. The present paper tries to close that gap. It reviews the building blocks needed to assemble a panoptic segmentation pipeline and the related literature. Moreover, a modular pipeline is set up to perform comprehensive, systematic experiments to assess the state of panoptic segmentation in the context of street mapping. As a byproduct, we also provide the first public dataset for that task, by extending the NPM3D dataset to include instance labels. That dataset and our source code are publicly available. We discuss which adaptations are need to adapt current panoptic segmentation methods to outdoor scenes and large objects. Our study finds that for mobile mapping data, KPConv performs best but is slower, while PointNet++ is fastest but performs significantly worse. Sparse CNNs are in between. Regardless of the backbone, Instance segmentation by clustering embedding features is better than using shifted coordinates

When LLMs step into the 3D world: a survey and meta-analysis of 3D tasks via multi-modal Large Language Models

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we underscore their potential to significantly advance spatial comprehension and interaction within embodied Artificial Intelligence (AI) systems. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs). It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue, as well as LLM-based agents for spatial reasoning, planning, and navigation. The paper also includes a brief review of other methods that integrate 3D and language. The meta-analysis presented in this paper reveals significant progress yet underscores the necessity for novel approaches to harness the full potential of 3D-LLMs. Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world. To support this survey, we have established a project page where papers related to our topic are organized and listed: https://github.com/ActiveVisionLab/Awesome-LLM-3D